Sparse Pinball Twin Bounded Support Vector Clustering

• Published in: IEEE transactions on computational social systems 2022-12, Vol.9 (6), p.1820-1829
• Main Authors: Tanveer, M., Tabish, M., Jangir, Jatin
• Format: Article
• Language: English
• Subjects: Algorithms; Clustering; Clustering algorithms; Clustering methods; concave convex procedure (CCCP); Datasets; Kernel; Machine learning; Machine learning algorithms; Marketing; Medical imaging; Noise sensitivity; Principles; Resampling; Sparsity; Stability analysis; Static VAr compensators; Support vector machines; support vector machines (SVMs)
• ISSN: 2329-924X; 2373-7476
• DOI: 10.1109/TCSS.2021.3122828

Analyzing unlabeled data is of prime importance in machine learning. Creating groups and identifying an underlying clustering principle is essential to many fields, such as biomedical analysis and market research. Novel unsupervised machine learning algorithms, also called clustering algorithms, are developed and utilized for this task. Inspired by twin support vector machine (TWSVM) principles, a recently introduced plane-based clustering algorithm, the twin bounded support vector clustering (TBSVC), is used in widespread clustering problems. However, TBSVC is sensitive to noise and suffers from low resampling stability due to usage of hinge loss. Pinball loss is another type of loss function that is less sensitive toward noise in the datasets and is more stable for resampling of datasets. However, the use of pinball loss negatively affects the sparsity of the solution of the problem. In this article, we present a novel plane-based clustering method, the sparse TBSVC using pinball loss (pinSTBSVC). The proposed pinSTBSVC is the sparse version of our recently proposed TBSVC using pinball loss (pinTBSVC). Sparse solutions help create better-generalized solutions to clustering problems; hence, we attempt to use the \epsilon -insensitive pinball loss function to propose pinSTBSVC. The loss function used to propose pinSTBSVC provides sparsity to the solution of the problem and improves the aforementioned plane-based clustering algorithms. Experimental results performed on benchmark University of California, Irvine (UCI) datasets indicate that the proposed method outperforms other existing plane-based clustering algorithms. Additionally, we also give the application of our method in biomedical image clustering and marketing science. We show that the proposed method is more accurate on real-world datasets too. The code for the proposed algorithm is also provided on the author's Github page: https://github.com/mtanveer1 .